Tuning fuzzy PID controllers using ant colony optimization

Authors:
Hamid Boubertakh;Mohamed Tadjine;Pierre-Yves Glorennec;Salim Labiod
Affiliations:
LAMEL, FSI, University of Jijel, BP. 98, Ouled Aissa, 18000, Algeria;LCP, Department of Electrical Engineering, ENP, France;INSA de Rennes, France;LAMEL, FSI, University of Jijel, Algeria
Venue:
MED '09 Proceedings of the 2009 17th Mediterranean Conference on Control and Automation
Year:
2009

Citing 0
Cited 3

PID step response using genetic programming

SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Fuzzy sliding-mode control for ball and beam system with fuzzy ant colony optimization

Expert Systems with Applications: An International Journal
Bio-inspired multi-agent systems for reconfigurable manufacturing systems

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Ant colony optimization (ACO) is one of the swarm intelligence (SI) techniques. It is a bio-inspired optimization method that has proven its success through various combinatorial optimization problems. This paper proposes an ant colony optimization algorithm for tuning fuzzy PID controllers. First, the design of typical Takagi-Sugeno (TS) fuzzy PID controllers is investigated. The tuning parameters of these controllers have physical meaning which makes its tuning task easier than conventional PID controllers. Simulation examples are provided to illustrate the efficiency of the proposed method.